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DBMS > Apache Impala vs. EsgynDB vs. HEAVY.AI vs. Pinecone

System Properties Comparison Apache Impala vs. EsgynDB vs. HEAVY.AI vs. Pinecone

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonEsgynDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonPinecone  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareA managed, cloud-native vector database
Primary database modelRelational DBMSRelational DBMSRelational DBMSVector DBMS
Secondary database modelsDocument storeSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score10.63
Rank#40  Overall
#24  Relational DBMS
Score0.15
Rank#325  Overall
#144  Relational DBMS
Score1.41
Rank#153  Overall
#71  Relational DBMS
Score3.02
Rank#87  Overall
#3  Vector DBMS
Websiteimpala.apache.orgwww.esgyn.cngithub.com/­heavyai/­heavydb
www.heavy.ai
www.pinecone.io
Technical documentationimpala.apache.org/­impala-docs.htmldocs.heavy.aidocs.pinecone.io/­docs/­overview
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaEsgynHEAVY.AI, Inc.Pinecone Systems, Inc
Initial release2013201520162019
Current release4.1.0, June 20225.10, January 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache Version 2; enterprise edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++, JavaC++ and CUDA
Server operating systemsLinuxLinuxLinuxhosted
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesString, Number, Boolean
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nononono
Secondary indexesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsyesyesno
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
ODBC
JDBC
ODBC
Thrift
Vega
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCAll languages supporting JDBC/ODBC/ADO.NetAll languages supporting JDBC/ODBC/Thrift
Python
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceJava Stored Proceduresno
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoRound robin
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication between multi datacentersMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache ImpalaEsgynDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Pinecone
DB-Engines blog posts

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2 June 2023, Matthias Gelbmann

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